The present invention relates generally to machine condition monitoring and more particularly to trend analysis using discontinuity detection for machine condition prognosis.
Machine condition monitoring (MCM) is the process of monitoring one or more parameters of machinery, such that a significant change in the machine parameter(s) is indicative of a current or developing condition (e.g., failure, fault, etc.). Such machinery includes rotating and stationary machines, such as turbines, boilers, heat exchangers, etc. Machine parameters of monitored machines may be vibrations, temperatures, friction, electrical usage, power consumption, sound, etc., which may be monitored by appropriate sensors. The output of the sensors may be in the form of and/or be aggregated into a sensor signal or a similar signal.
Generally, a condition is a comparison of the machine parameter to a threshold. For example, a machine parameter value may be compared with an equality and/or inequality operator, such as <, =, >, ≠, ≡, ≦, ≧, etc., to a threshold value. Therefore, a condition signal is a signal based on the machine parameter values (e.g., a plurality of machine parameter values grouped as a discrete or continuous signal). Since machine sensor are subject to certain amounts of uncertainty, error, noise, and the like, condition signals are composed of an actual signal as well as some amount of noise.
Machine condition monitoring systems generally use a number of rules to define the machine parameters to be monitored and critical information (e.g., indicative of a condition change) about those machine parameters. In some cases, hundreds of sensors monitor and/or record these machine parameters. The output of the sensors (e.g., sensor signal, sensor estimate, sensor residue, etc.) may then be used as the input to one or more rules. Rules are used to detect faults, but must minimize improper indicators of faults (e.g., false alarms). In general, simple rules are constructed as indicative conditional logical operations (e.g., if-then statements). The input of a rule, the “if”, is a condition as described above (e.g., if machine parameter A>threshold B) and the output of the rule, the “then”, is a fault (e.g., then fault type 1).
Fault prognosis in MCM is used to predict the future parameter values and/or condition signal of a machine. That is, fault prognosis attempts to determine when a fault condition or other significant machine event will occur. Preventative maintenance or other action may be taken to prevent these faults.
Presently, machine condition monitoring fault prognosis relies on trend analysis of condition signals. Current and former machine parameter values are analyzed to determine a condition trend. The condition trend is determined by determining an equation, such as a polynomial equation, approximating the trend. Based on this trend analysis, a future condition of the machine may be determined. However, such basic trend analysis fails to account for discontinuities in condition signals. That is, current trend analysis in MCM is subject to falsely predicting trends and thus mistakenly predicting failure times.
Therefore, alternative methods are required to analyze trends and detect discontinuities in machine condition monitoring.